RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / NVIDIA RTX PRO 6000 Blackwell

What can NVIDIA RTX PRO 6000 Blackwell run?

Build: RTX PRO 6000 Blackwell + Threadripper PRO + 128GB

Memory: 96 GB VRAM + 128 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
283 models

Full-VRAM resident, with room for context. No compromises.

#1all-MiniLM-L6-v2
0.022B
other
Commercial OK
Quant: Q4_K_MContext: 256VRAM: 1.8 GBHeadroom: 94.2 GB
87694
tok/s
Estimated
Weights
0.01 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#2Piper
0.025B
other
Commercial OK
Quant: Q4_K_MContext: 0VRAM: 1.8 GBHeadroom: 94.2 GB
77171
tok/s
Estimated
Weights
0.02 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#3Whisper Tiny
0.039B
other
Commercial OK
Quant: Q4_K_MContext: 30VRAM: 1.8 GBHeadroom: 94.2 GB
49469
tok/s
Estimated
Weights
0.02 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#4Whisper Base
0.074B
other
Commercial OK
Quant: Q4_K_MContext: 30VRAM: 1.8 GBHeadroom: 94.2 GB
26071
tok/s
Estimated
Weights
0.04 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#5Kokoro 82M
0.082B
other
Commercial OK
Quant: Q4_K_MContext: 0VRAM: 1.9 GBHeadroom: 94.1 GB
23528
tok/s
Estimated
Weights
0.05 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#6all-mpnet-base-v2
0.109B
other
Commercial OK
Quant: Q4_K_MContext: 384VRAM: 1.9 GBHeadroom: 94.1 GB
17700
tok/s
Estimated
Weights
0.07 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#7paraphrase-multilingual-MiniLM-L12-v2
0.118B
other
Commercial OK
Quant: Q4_K_MContext: 128VRAM: 1.9 GBHeadroom: 94.1 GB
16350
tok/s
Estimated
Weights
0.07 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#8Nomic Embed Text v1.5
0.137B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.0 GBHeadroom: 94.0 GB
14082
tok/s
Estimated
Weights
0.08 GB
KV cache
0.07 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#9SmolLM2 135M Instruct
0.135B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.0 GBHeadroom: 94.0 GB
14291
tok/s
Estimated
Weights
0.08 GB
KV cache
0.07 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#10GTE ModernBERT Base
0.149B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.0 GBHeadroom: 94.0 GB
12948
tok/s
Estimated
Weights
0.09 GB
KV cache
0.07 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#11Whisper Small
0.244B
other
Commercial OK
Quant: Q4_K_MContext: 30VRAM: 2.0 GBHeadroom: 94.0 GB
7907
tok/s
Estimated
Weights
0.15 GB
KV cache
0.00 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#12Gemma 3 270M
0.27B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.1 GBHeadroom: 93.9 GB
7145
tok/s
Estimated
Weights
0.16 GB
KV cache
0.14 GB
Activations
0.02 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
10 models

Tight VRAM, partial CPU offload, or context-limited.

GLM-5
200B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 153.6 GBHeadroom: 19.2 GB
  • • Partial CPU offload: ~37% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
3
tok/s
Estimated
Weights
120.75 GB
KV cache
25.00 GB
Activations
6.04 GB
Runtime
1.80 GB
Model details →
Nemotron 3 Super (120B-A12B)
120B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 92.9 GBHeadroom: 3.1 GB
  • • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run nemotron3:super
16
tok/s
Estimated
Weights
72.45 GB
KV cache
15.00 GB
Activations
3.62 GB
Runtime
1.80 GB
Model details →
Mistral Large 2 (123B)
123B
mistral
Quant: Q4_K_MContext: 2,048VRAM: 95.2 GBHeadroom: 0.8 GB
  • • Tight VRAM fit — only 0.8 GB headroom left for context growth
ollama run mistral-large:123b
16
tok/s
Estimated
Weights
74.26 GB
KV cache
15.38 GB
Activations
3.72 GB
Runtime
1.80 GB
Model details →
Mixtral 8x22B Instruct
141B
mixtral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 161.7 GBHeadroom: 11.1 GB
  • • Partial CPU offload: ~41% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run mixtral:8x22b
5
tok/s
Estimated
Weights
85.13 GB
KV cache
70.50 GB
Activations
4.26 GB
Runtime
1.80 GB
Model details →
WizardLM-2 8x22B
141B
wizard
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 161.7 GBHeadroom: 11.1 GB
  • • Partial CPU offload: ~41% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
5
tok/s
Estimated
Weights
85.13 GB
KV cache
70.50 GB
Activations
4.26 GB
Runtime
1.80 GB
Model details →
Command R+ (Aug 2024)
104B
command-r
Quant: AWQ-INT4Context: 8,192VRAM: 163.0 GBHeadroom: 9.8 GB
  • • Partial CPU offload: ~41% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
4
tok/s
Estimated
Weights
104.00 GB
KV cache
52.00 GB
Activations
5.21 GB
Runtime
1.80 GB
Model details →
DBRX Instruct
132B
dbrx
Commercial OK
Quant: AWQ-INT4Context: 2,048VRAM: 156.9 GBHeadroom: 15.9 GB
  • • Partial CPU offload: ~39% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
11
tok/s
Estimated
Weights
132.00 GB
KV cache
16.50 GB
Activations
6.60 GB
Runtime
1.80 GB
Model details →
DBRX Base
132B
dbrx
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 151.5 GBHeadroom: 21.3 GB
  • • Partial CPU offload: ~37% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
19
tok/s
Estimated
Weights
79.69 GB
KV cache
66.00 GB
Activations
3.99 GB
Runtime
1.80 GB
Model details →

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

+32 GB system RAM

~$80–150

Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 14 new tradeoff

  • • Qwen 3 235B-A22B
  • • GLM-5
  • • Nemotron 3 Super (120B-A12B)
  • • Mistral Large 2 (123B)
Shop this upgrade↗

Upgrade to NVIDIA H200 NVL (PCIe)

~$32000

141 GB VRAM (vs your 96 GB) plus a bandwidth jump from ~1792 GB/s to ~4800 GB/s.

Unlocks: 6 new comfortable

  • • Mistral Large 2 (123B)
  • • Mixtral 8x22B Instruct
  • • WizardLM-2 8x22B
  • • Command R+ (Aug 2024)
Shop this upgrade↗

Add a second NVIDIA RTX PRO 6000 Blackwell

~$8999

Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.

Unlocks: 14 new comfortable

  • • Nemotron 3 Super (120B-A12B)
  • • Mistral Large 2 (123B)
  • • Mixtral 8x22B Instruct
  • • WizardLM-2 8x22B
Shop this upgrade↗

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)
1600B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

—
Qwen 3.5 235B-A17B (MoE)
397B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

—
Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

—
DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

—

How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated
Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

RunLocalAI Will-It-Run Framework →

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